Alberta Oil Sands Royalty Data
Andrew Leach
04 February, 2023
One of the recommendations of the Alberta Royalty Review in 2015-16 was more transparency with respect to oil sands costs, profits, production and royalty payments. The Alberta Government has followed that recommendation and makes project-level data available on their open data site. This document compiles those data and provides some basic graphs that you might find useful. Where appropriate, I’ve provided a link to the R code used to process the data.
library(janitor)
library(scales)
library(tidyverse)
library(readxl)
library(openxlsx)
library(viridis)
library(ggthemes)
work_theme<-function(){
theme_tufte(18)+
theme(
plot.subtitle = element_text(color="grey10",size=rel(1)),
plot.title = element_text(face="bold"),
plot.caption = element_text(color="grey50",size=rel(1)),
legend.title = element_text(color="grey10",size=rel(1.5)),
legend.text = element_text(color="grey10",size=rel(1.5)),
strip.text = element_text(size=rel(1.2)),
axis.title = element_text(size=rel(1.2)),
axis.text = element_text(size=rel(1.2)),
axis.text.x = element_text(size=rel(.8)),
axis.ticks = element_blank(),
panel.spacing = unit(.75,"lines"),
legend.position = "bottom",
plot.margin = margin(t = .5, r = .5, b = .5, l = .5,unit= "cm"),
#axis.text.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)
NULL
)+
NULL
}
colors_tableau10<-function ()
{
return(c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD",
"#8C564B", "#E377C2", "#7F7F7F", "#BCBD22", "#17BECF"))
}Download and Combine the Annual Data for 2016-2021
The first step is to load and process the data. The code button provides a window into how I’ve done this.
#load plant data
make_os_data<-function(){
os_data_2021 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2021 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
os_data_2020 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2020 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
os_data_2019 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2019 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
os_data_2018 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2018 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
os_data_2017 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2017 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
os_data_2016 <- read.xlsx(xlsxFile = "royalty_data.xlsx", sheet = "2016 Royalty Data", startRow = 1,skipEmptyRows = TRUE,detectDates = TRUE)
#stack all the years together
os_data<-bind_rows(os_data_2021,os_data_2020,os_data_2019,os_data_2018,os_data_2017,os_data_2016)
#clean the names
os_data<-os_data %>% clean_names()%>%
mutate(op_profit=gross_revenue-operating_costs-capital_costs-other_costs+other_net_proceeds)
# data are reported for an individual project twice if it passes payout in a given year
# see here for a test
#os_data %>% group_by(project,reporting_year) %>% select(project,project_name,reporting_year,payout_status) %>% mutate(n_obs=n())%>% filter(n_obs>1) %>% arrange(-n_obs)
# combine the data and include two things: a payout year indicator and a net and gross rev royalty number along with a total royalty paid
os_data <- os_data %>% group_by(project,reporting_year,project_name,operator_name)%>%
summarize(
project_revenue=sum(project_revenue),
gross_revenue=sum(gross_revenue),
op_profit=sum(op_profit),
cleaned_crude_bitumen_at_rcp_barrels=sum(cleaned_crude_bitumen_at_rcp_barrels),
operating_costs=sum(operating_costs),
capital_costs=sum(capital_costs),
return_allowance=sum(return_allowance),
other_costs=sum(other_costs),
other_net_proceeds=sum(other_net_proceeds),
net_revenue=sum(net_revenue),
royalty_type=last(royalty_type),
royalty_type_start=first(royalty_type),
payout_start=first(payout_status),
payout_status=last(payout_status),
net_rev_royalty=sum(royalty_payable*(royalty_type=="NET")),
gross_rev_royalty=sum(royalty_payable*(royalty_type=="GROSS")),
royalty_payable=sum(royalty_payable),
royalty_rate=last(royalty_rate_percent),
first_royalty_rate=first(royalty_rate_percent),
royalty_avg=royalty_payable/gross_revenue,
payout_year=(n()>1),
unrecovered_balance_net_loss_at_eop=last(unrecovered_balance_net_loss_at_eop))%>%
mutate(
last_prod=last(cleaned_crude_bitumen_at_rcp_barrels)/365,
label=paste(project_name,"\n(",formatC(round(last_prod,digits=-3),format="f", big.mark=",",digits = 0)," bbl/d)",sep=""))%>%
ungroup()%>%
group_by(project)%>%
mutate(project_payout=(last(payout_status)=="POST"))
#fix facilities
os_data <- os_data %>% mutate(
royalty_bbl=royalty_payable/cleaned_crude_bitumen_at_rcp_barrels,
op_costs_bbl=operating_costs/cleaned_crude_bitumen_at_rcp_barrels,
gross_revenue_bbl=gross_revenue/cleaned_crude_bitumen_at_rcp_barrels,
cap_costs_bbl=capital_costs/cleaned_crude_bitumen_at_rcp_barrels,
project_name=gsub(" Project","",project_name),
project_name=gsub("Christina Lake Regional","Christina Lake (MEG)",project_name),
project_name=gsub("Christina Lake Thermal","Christina Lake (CVE)",project_name),
project_name=gsub("MacKay River Commercial","PetroChina",project_name),
project_name=gsub("MacKay River","MacKay River (Suncor)",project_name),
project_name=gsub("PetroChina","MacKay River (PetroChina)",project_name),
project_name=gsub(" Thermal","",project_name),
project_name=gsub(" Mine","",project_name),
project_name=gsub(" Oil Sands","",project_name),
project_name=gsub(" EOR","",project_name),
project_name=gsub(" Commercial","",project_name),
project_name=gsub(" SAGD","",project_name),
project_name=gsub(" Demonstration","",project_name),
project_name=gsub(" In-Situ","",project_name),
project_name=as.factor(project_name),
op_profit_net=op_profit-royalty_payable,
op_profit_bbl=op_profit/cleaned_crude_bitumen_at_rcp_barrels,
op_profit_net_bbl=op_profit_net/cleaned_crude_bitumen_at_rcp_barrels
)
}
os_data<-make_os_data()
mines<-c("Muskeg River","Fort Hills","Kearl","Horizon","Muskeg River","Jackpine","Syncrude","Suncor")
SAGD<-c("Hangingstone","Leismer","Blackrod","Mackay River","Kirby","Christina Lake","Foster Creek",
"Long Lake","Great Divide","Surmont","Jackfish","Sunrise","Orion","Firebag","MacKay River",
"West Ells","SAGD","Tucker","Narrows Lake")
os_data<-os_data %>% mutate(mine=(project_name %in% mines),
in_situ=(!project_name %in% mines),
)%>%
group_by(project_name)%>%
mutate(min_bbls=min(cleaned_crude_bitumen_at_rcp_barrels)/365,
max_bbls=max(cleaned_crude_bitumen_at_rcp_barrels)/365)%>%
ungroup()%>%
mutate(project=(max_bbls>8000),
big_project=(min_bbls>30000),
med_project=(min_bbls>8000)&(max_bbls<50000)
)Production
ggplot(os_data %>% filter(project,mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),last_prod/1000,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=-90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Bitumen Production (thousands of barrels per day)",
title="Annual Bitumen Production, Oil Sands Mining Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(big_project,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),last_prod/1000,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 8, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Bitumen Production (thousands of barrels per day)",
title="Annual Bitumen Production, Larger Oil Sands In Situ Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
Revenue per Barrel
ggplot(os_data %>% filter(mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),gross_revenue_bbl,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Revenue per barrel bitumen (CA$/bbl)",
title="Gross Revenue per Barrel Bitumen, Oil Sands Mining Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(big_project,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),gross_revenue_bbl,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Revenue per barrel bitumen (CA$/bbl)",
title="Gross Revenue per Barrel Bitumen, Larger In Situ Oil Sands Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
Operating Costs
ggplot(os_data %>% filter(mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),op_costs_bbl,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Operating cost per barrel bitumen (CA$/bbl)",
title="Operating Costs, Oil Sands Mining Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(big_project,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod)
),
aes(factor(reporting_year),op_costs_bbl,fill=project_type))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
scale_fill_brewer()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Operating cost per barrel bitumen (CA$/bbl)",
title="Operating Costs, Larger In Situ Oil Sands Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
Operating Cost Density Plot
ggplot(os_data%>%filter(big_project) %>% group_by(reporting_year) %>%
mutate(year_total=sum(cleaned_crude_bitumen_at_rcp_barrels), weight=cleaned_crude_bitumen_at_rcp_barrels/year_total) %>%
ungroup()%>%
mutate(mine=as.factor(mine),
mine=fct_recode(mine,Mine="TRUE","In Situ"="FALSE"))
, aes(op_costs_bbl,group=factor(reporting_year),weights=weight))+
stat_density(aes(color=factor(reporting_year)),geom="line",position = "identity",trim=T,size=1.6)+
scale_colour_manual(NULL,values=colors_tableau10())+
facet_wrap(~mine,scales="free_x")+
expand_limits(x=0)+
guides(color=guide_legend(nrow=1))+
#geom_density(aes(color=factor(reporting_year)), alpha=0.8) +
labs(title="Density plot of oil sands operating costs per barrel bitumen",
subtitle="Production-weighted, for projects with more than 10,000 barrels per day of bitumen production",
caption="Source: Government of Alberta 2016 and 2017 Royalty Data, graph by Andrew Leach",
x="Operating Costs ($Cdn/bbl bitumen)",
fill="Reporting Year")+
work_theme()
Royalties
ggplot(os_data %>% filter(mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),royalty_bbl,fill=payout))+
scale_fill_manual("",values=c("PRE"="lightgreen","POST"="darkgreen"),labels=c("Pre-Payout","Post-Payout"))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
guides(fill=guide_legend(nrow = 1),color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Royalties payable per barrel bitumen (CA$/bbl)",
title="Royalties Payable per Barrel Bitumen, Oil Sands Mining Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(big_project,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),royalty_bbl,fill=payout))+
scale_fill_manual("",values=c("PRE"="lightgreen","POST"="darkgreen"),labels=c("Pre-Payout","Post-Payout"))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
guides(fill=guide_legend(nrow = 1),color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Royalties payable per barrel bitumen (CA$/bbl)",
title="Royalties Payable per Barrel Bitumen, Larger In Situ Oil Sands Projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
Operating Profits (Post-Royalty)
ggplot(os_data %>% filter(mine,project_name!="Fort Hills")%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),op_profit_net_bbl,fill=payout))+
scale_fill_manual("",values=c("PRE"="lightgreen","POST"="darkgreen"),labels=c("Pre-Payout","Post-Payout"))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
guides(fill=guide_legend(nrow = 1),color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Operating profit per barrel bitumen (CA$/bbl)",
title="Operating Profit per Barrel Bitumen, Oil Sands Mining Projects",
subtitle="Gross revenue net operating and capital costs and royalties, excluding Fort Hills",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(big_project,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),op_profit_net_bbl,fill=payout))+
scale_fill_manual("",values=c("PRE"="lightgreen","POST"="darkgreen"),labels=c("Pre-Payout","Post-Payout"))+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
#scale_fill_viridis(discrete=FALSE,"Royalties Paid ($/bbl)")+
#coord_flip()+
guides(fill=guide_legend(nrow = 1),color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 7, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Operating profit per barrel bitumen (CA$/bbl)",
title="Operating Profit per Barrel Bitumen, Oil Sands In-Situ Projects",
subtitle="Gross revenue net operating and capital costs and royalties",
caption="Data via Government of Alberta, graph by @andrew_leach")
Unrecovered capital costs
ggplot(os_data %>% filter(project,!project_payout,mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),unrecovered_balance_net_loss_at_eop/10^6,fill=mine))+
#scale_fill_manual("",values=c("FALSE"="lightgreen","TRUE"="darkgreen"),labels=c("In Situ","Mine"))+
#scale_fill_brewer("Project type")+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
scale_fill_brewer()+
#coord_flip()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 1,labeller = label_wrap_gen(width = 10, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Unrecovered capital costs (CA$ million)",
title="Unrecovered capital costs per royalty formula, oil sands mining projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
ggplot(os_data %>% filter(project,!project_payout,!mine)%>%
mutate(project_type=ifelse(mine,"Mine","In Situ"),
project_name_factor=as.factor(project_name),
project_name_factor=fct_reorder(project_name_factor,last_prod),
payout=as_factor(payout_status),
payout=fct_relevel(payout,"PRE")
),
aes(factor(reporting_year),unrecovered_balance_net_loss_at_eop/10^6,fill=mine))+
#scale_fill_manual("",values=c("FALSE"="lightgreen","TRUE"="darkgreen"),labels=c("In Situ","Mine"))+
#scale_fill_brewer("Project type")+
geom_col(size=0.25,position = position_dodge(width = .25),color="black")+
#scale_color_viridis("Royalties Paid\nPer Barrel\nCleaned Bitumen")+
scale_fill_brewer()+
#coord_flip()+
guides(fill="none",color="none")+
facet_wrap(~project_name_factor,nrow = 2,labeller = label_wrap_gen(width = 8, multi_line = TRUE))+
#scale_x_reverse()+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(x=NULL,y="Unrecovered capital costs (CA$ million)",
title="Unrecovered capital costs per royalty formula, larger in situ oil sands projects",
#subtitle="Excluding Electricity,by NAICS 4-Digit Code",
caption="Data via Government of Alberta, graph by @andrew_leach")
GHG emissions intensity
#load plant data
os_ghg_data <- read_excel(path = "ab_os_ghgs.xlsx", sheet = "Emission Intensity", range="A4:AO35")%>%
clean_names()%>% pivot_longer(cols=seq(15,23),names_to="year",values_to="adj_ghg")%>%
select(company,facility,subsector,product,year,adj_ghg)%>%mutate(year=str_sub(year,start=2,end=5))
os_prod_data <- read_excel(path = "ab_os_ghgs.xlsx", sheet = "Emission Intensity", range="A4:AO35")%>%
clean_names()%>% pivot_longer(cols=seq(24,32),names_to="year",values_to="prod")%>%
select(company,facility,subsector,product,year,prod)%>%mutate(year=str_sub(year,start=2,end=5))
os_ei_data <- read_excel(path = "ab_os_ghgs.xlsx", sheet = "Emission Intensity", range="A4:AO35")%>%
clean_names()%>% pivot_longer(cols=seq(33,41),names_to="year",values_to="ei")%>%
select(company,facility,subsector,product,year,ei)%>%mutate(year=str_sub(year,start=2,end=5)) %>%
left_join(os_ghg_data)%>%
left_join(os_prod_data)
#keep anything larger than tucker
os_big_projects<- os_ei_data %>% filter(year==2019) %>% group_by(subsector)%>% filter(prod>1250000)
os_ei_data %>% mutate(facility=factor(facility),facility=fct_other(facility,keep = os_big_projects$facility))%>%
group_by(facility,subsector,product,year) %>% summarize(prod=sum(prod),ei=sum(adj_ghg)/sum(prod),adj_ghg=sum(adj_ghg))%>%
mutate(facility=fct_recode(facility, "Foster Creek" = "Foster Creek SAGD Bitumen Battery (with Cogen)",
"Canadian Natural AOSP"="Canadian Natural Upgrading Limited Muskeg River Mine and Jackpine Mine and Scotford Upgrader",
"MEG Christina Lake"="MEG Christina Lake Regional project",
"Cenovus Christina Lake"="Christina Lake SAGD Bitumen Battery",
"Hangingstone"="Hangingstone Expansion project",
"MacKay River"="MacKay River, In-Situ Oil Sands Plant",
"Surmont"="Surmont SAGD Commercial Battery"))%>%
group_by(facility) %>% mutate(prod_2019=sum(prod*(year==2019)*6.2929/365))%>%
mutate(label2=paste(facility,"\n(",formatC(round(prod_2019,digits=-3),format="f", big.mark=",",digits = 0)," bbl/d)",sep=""),
label=facility)%>%
filter(subsector!="In Situ",ei<2)%>%
ggplot()+
geom_col(aes(year,ei,fill="A"),size=0.25,position = position_dodge(width = .25),color="black")+
scale_fill_brewer()+
#geom_hline(aes(yintercept = 32.65*1.26,colour="Current $CA bitumen value"))+
facet_wrap(~label,nrow = 1,labeller = label_wrap_gen(width = 12, multi_line = TRUE))+
#scale_x_reverse()+
#coord_flip()+
#scale_fill_viridis("Reporting Year",discrete = T)+
guides(colour=guide_legend(),fill=FALSE)+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(y="Cogen-adjusted emissions per barrel (t/bbl)",x=NULL,
title="2011-2019 Emissions Intensity by Oil Sands Mining project",
#subtitle="projects with production above 40,000 bbl/d in every year. 2019 production, rounded to the nearest thousand barrels per day, shown in brackets.",
caption="Source: Alberta Government data, graph by @andrew_leach")
os_ei_data %>% mutate(facility=factor(facility),facility=fct_other(facility,keep = os_big_projects$facility))%>%
group_by(facility,subsector,product,year) %>% summarize(prod=sum(prod),ei=sum(adj_ghg)/sum(prod),adj_ghg=sum(adj_ghg))%>%
mutate(facility=fct_recode(facility, "Foster Creek" = "Foster Creek SAGD Bitumen Battery (with Cogen)",
"Canadian Natural AOSP"="Canadian Natural Upgrading Limited Muskeg River Mine and Jackpine Mine and Scotford Upgrader",
"MEG Christina Lake"="MEG Christina Lake Regional project",
"Cenovus Christina Lake"="Christina Lake SAGD Bitumen Battery",
"Hangingstone"="Hangingstone Expansion project",
"MacKay River"="MacKay River, In-Situ Oil Sands Plant",
"Surmont"="Surmont SAGD Commercial Battery"))%>%
group_by(facility) %>% mutate(prod_2019=sum(prod*(year==2019)*6.2929/365))%>%
mutate(label2=paste(facility,"\n(",formatC(round(prod_2019,digits=-3),format="f", big.mark=",",digits = 0)," bbl/d)",sep=""),
label=facility)%>%
filter(subsector=="In Situ",ei<2)%>%
mutate(facility=fct_relevel(facility,after = Inf))%>%
ggplot()+
geom_col(aes(year,ei,fill="A"),size=0.25,position = position_dodge(width = .25),color="black")+
scale_fill_brewer()+
#geom_hline(aes(yintercept = 32.65*1.26,colour="Current $CA bitumen value"))+
facet_wrap(~label,nrow = 3,labeller = label_wrap_gen(width = 12, multi_line = TRUE))+
scale_colour_manual("",values="black")+
#scale_x_reverse()+
#coord_flip()+
#scale_fill_viridis("Reporting Year",discrete = T)+
guides(colour=guide_legend(),fill=FALSE)+
work_theme()+
theme(legend.position="bottom",
axis.text.x = element_text(angle=90,hjust = 0.5,vjust=0.5)
)+
labs(y="Cogen-adjusted emissions per barrel (t/bbl)",x=NULL,
title="2011-2019 Emissions Intensity by Oil Sands In-Situ project",
#subtitle="projects with production above 40,000 bbl/d in every year. 2019 production, rounded to the nearest thousand barrels per day, shown in brackets.",
caption="Source: Alberta Government data, graph by @andrew_leach")